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  <h1>Source code for torch_tensorrt._compile</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span><span class="p">,</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">Any</span>
<span class="kn">from</span> <span class="nn">torch_tensorrt</span> <span class="kn">import</span> <span class="n">_enums</span>
<span class="kn">import</span> <span class="nn">torch_tensorrt.ts</span>
<span class="kn">from</span> <span class="nn">torch_tensorrt</span> <span class="kn">import</span> <span class="n">logging</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch.fx</span>
<span class="kn">from</span> <span class="nn">enum</span> <span class="kn">import</span> <span class="n">Enum</span>

<span class="kn">import</span> <span class="nn">torch_tensorrt.fx</span>
<span class="kn">from</span> <span class="nn">torch_tensorrt.fx.utils</span> <span class="kn">import</span> <span class="n">LowerPrecision</span>


<span class="k">class</span> <span class="nc">_IRType</span><span class="p">(</span><span class="n">Enum</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;Enum to set the minimum required logging level to print a message to stdout&quot;&quot;&quot;</span>

    <span class="n">ts</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="n">fx</span> <span class="o">=</span> <span class="mi">1</span>


<span class="k">class</span> <span class="nc">_ModuleType</span><span class="p">(</span><span class="n">Enum</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;Enum to set the minimum required logging level to print a message to stdout&quot;&quot;&quot;</span>

    <span class="n">nn</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="n">ts</span> <span class="o">=</span> <span class="mi">1</span>
    <span class="n">fx</span> <span class="o">=</span> <span class="mi">2</span>


<span class="k">def</span> <span class="nf">_parse_module_type</span><span class="p">(</span><span class="n">module</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">_ModuleType</span><span class="p">:</span>
    <span class="k">if</span> <span class="nb">any</span><span class="p">(</span>
        <span class="nb">isinstance</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">t</span><span class="p">)</span>
        <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">ScriptModule</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">ScriptFunction</span><span class="p">]</span>
    <span class="p">):</span>
        <span class="k">return</span> <span class="n">_ModuleType</span><span class="o">.</span><span class="n">ts</span>
    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">):</span>
        <span class="k">return</span> <span class="n">_ModuleType</span><span class="o">.</span><span class="n">fx</span>
    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
        <span class="k">return</span> <span class="n">_ModuleType</span><span class="o">.</span><span class="n">nn</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;Module is an unknown format&quot;</span><span class="p">)</span>


<span class="k">def</span> <span class="nf">_get_target_ir</span><span class="p">(</span><span class="n">module_type</span><span class="p">:</span> <span class="n">_ModuleType</span><span class="p">,</span> <span class="n">ir</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">_IRType</span><span class="p">:</span>
    <span class="n">module_is_tsable</span> <span class="o">=</span> <span class="nb">any</span><span class="p">([</span><span class="n">module_type</span> <span class="o">==</span> <span class="n">t</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="p">[</span><span class="n">_ModuleType</span><span class="o">.</span><span class="n">nn</span><span class="p">,</span> <span class="n">_ModuleType</span><span class="o">.</span><span class="n">ts</span><span class="p">]])</span>
    <span class="n">module_is_fxable</span> <span class="o">=</span> <span class="nb">any</span><span class="p">([</span><span class="n">module_type</span> <span class="o">==</span> <span class="n">t</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="p">[</span><span class="n">_ModuleType</span><span class="o">.</span><span class="n">nn</span><span class="p">,</span> <span class="n">_ModuleType</span><span class="o">.</span><span class="n">fx</span><span class="p">]])</span>

    <span class="n">ir_targets_torchscript</span> <span class="o">=</span> <span class="nb">any</span><span class="p">([</span><span class="n">ir</span> <span class="o">==</span> <span class="n">opt</span> <span class="k">for</span> <span class="n">opt</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;torchscript&quot;</span><span class="p">,</span> <span class="s2">&quot;ts&quot;</span><span class="p">]])</span>
    <span class="n">ir_targets_fx</span> <span class="o">=</span> <span class="n">ir</span> <span class="o">==</span> <span class="s2">&quot;fx&quot;</span>

    <span class="k">if</span> <span class="n">module_is_tsable</span> <span class="ow">and</span> <span class="n">ir_targets_torchscript</span><span class="p">:</span>
        <span class="k">return</span> <span class="n">_IRType</span><span class="o">.</span><span class="n">ts</span>
    <span class="k">elif</span> <span class="n">module_is_fxable</span> <span class="ow">and</span> <span class="n">ir_targets_fx</span><span class="p">:</span>
        <span class="k">return</span> <span class="n">_IRType</span><span class="o">.</span><span class="n">fx</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="k">if</span> <span class="n">ir</span> <span class="o">==</span> <span class="s2">&quot;default&quot;</span><span class="p">:</span>
            <span class="c1"># Options are listed in order of preference</span>
            <span class="k">if</span> <span class="n">module_is_tsable</span><span class="p">:</span>
                <span class="n">logging</span><span class="o">.</span><span class="n">log</span><span class="p">(</span>
                    <span class="n">logging</span><span class="o">.</span><span class="n">Level</span><span class="o">.</span><span class="n">Info</span><span class="p">,</span> <span class="s2">&quot;ir was set to default, using TorchScript as ir&quot;</span>
                <span class="p">)</span>
                <span class="k">return</span> <span class="n">_IRType</span><span class="o">.</span><span class="n">ts</span>
            <span class="k">elif</span> <span class="n">module_is_fxable</span><span class="p">:</span>
                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                    <span class="s2">&quot;Was given a torch.fx.GraphModule, fx is not currently supported by Torch-TensorRT&quot;</span>
                <span class="p">)</span>
                <span class="c1"># logging.log(logging.Level.Info, &quot;ir was set to default, using TorchScript as fx&quot;)</span>
                <span class="c1"># return _IRType.fx</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Module was provided with in an unsupported format&quot;</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Unknown ir was requested&quot;</span><span class="p">)</span>


<div class="viewcode-block" id="compile"><a class="viewcode-back" href="../../py_api/torch_tensorrt.html#torch_tensorrt.compile">[docs]</a><span class="k">def</span> <span class="nf">compile</span><span class="p">(</span>
    <span class="n">module</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span>
    <span class="n">ir</span><span class="o">=</span><span class="s2">&quot;default&quot;</span><span class="p">,</span>
    <span class="n">inputs</span><span class="o">=</span><span class="p">[],</span>
    <span class="n">enabled_precisions</span><span class="o">=</span><span class="nb">set</span><span class="p">([</span><span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">float</span><span class="p">]),</span>
    <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
<span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;Compile a PyTorch module for NVIDIA GPUs using TensorRT</span>

<span class="sd">    Takes a existing PyTorch module and a set of settings to configure the compiler</span>
<span class="sd">    and using the path specified in ``ir`` lower and compile the module to TensorRT</span>
<span class="sd">    returning a PyTorch Module back</span>

<span class="sd">    Converts specifically the forward method of a Module</span>

<span class="sd">    Arguments:</span>
<span class="sd">        module (Union(torch.nn.Module,torch.jit.ScriptModule): Source module</span>

<span class="sd">    Keyword Arguments:</span>
<span class="sd">        inputs (List[Union(torch_tensorrt.Input, torch.Tensor)]): **Required** List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using</span>
<span class="sd">            torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum</span>
<span class="sd">            to select device type. ::</span>

<span class="sd">                input=[</span>
<span class="sd">                    torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1</span>
<span class="sd">                    torch_tensorrt.Input(</span>
<span class="sd">                        min_shape=(1, 224, 224, 3),</span>
<span class="sd">                        opt_shape=(1, 512, 512, 3),</span>
<span class="sd">                        max_shape=(1, 1024, 1024, 3),</span>
<span class="sd">                        dtype=torch.int32</span>
<span class="sd">                        format=torch.channel_last</span>
<span class="sd">                    ), # Dynamic input shape for input #2</span>
<span class="sd">                    torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings</span>
<span class="sd">                ]</span>

<span class="sd">        enabled_precision (Set(Union(torch.dtype, torch_tensorrt.dtype))): The set of datatypes that TensorRT can use when selecting kernels</span>
<span class="sd">        ir (str): The requested strategy to compile. (Options: default - Let Torch-TensorRT decide, ts - TorchScript with scripting path)</span>
<span class="sd">        **kwargs: Additional settings for the specific requested strategy (See submodules for more info)</span>

<span class="sd">    Returns:</span>
<span class="sd">        torch.nn.Module: Compiled Module, when run it will execute via TensorRT</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">module_type</span> <span class="o">=</span> <span class="n">_parse_module_type</span><span class="p">(</span><span class="n">module</span><span class="p">)</span>
    <span class="n">target_ir</span> <span class="o">=</span> <span class="n">_get_target_ir</span><span class="p">(</span><span class="n">module_type</span><span class="p">,</span> <span class="n">ir</span><span class="p">)</span>
    <span class="k">if</span> <span class="n">target_ir</span> <span class="o">==</span> <span class="n">_IRType</span><span class="o">.</span><span class="n">ts</span><span class="p">:</span>
        <span class="n">ts_mod</span> <span class="o">=</span> <span class="n">module</span>
        <span class="k">if</span> <span class="n">module_type</span> <span class="o">==</span> <span class="n">_ModuleType</span><span class="o">.</span><span class="n">nn</span><span class="p">:</span>
            <span class="n">logging</span><span class="o">.</span><span class="n">log</span><span class="p">(</span>
                <span class="n">logging</span><span class="o">.</span><span class="n">Level</span><span class="o">.</span><span class="n">Info</span><span class="p">,</span>
                <span class="s2">&quot;Module was provided as a torch.nn.Module, trying to script the module with torch.jit.script. In the event of a failure please preconvert your module to TorchScript&quot;</span><span class="p">,</span>
            <span class="p">)</span>
            <span class="n">ts_mod</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span><span class="p">(</span><span class="n">module</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">ts</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span>
            <span class="n">ts_mod</span><span class="p">,</span> <span class="n">inputs</span><span class="o">=</span><span class="n">inputs</span><span class="p">,</span> <span class="n">enabled_precisions</span><span class="o">=</span><span class="n">enabled_precisions</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span>
        <span class="p">)</span>
    <span class="k">elif</span> <span class="n">target_ir</span> <span class="o">==</span> <span class="n">_IRType</span><span class="o">.</span><span class="n">fx</span><span class="p">:</span>
        <span class="k">if</span> <span class="p">(</span>
            <span class="n">torch</span><span class="o">.</span><span class="n">float16</span> <span class="ow">in</span> <span class="n">enabled_precisions</span>
            <span class="ow">or</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">half</span> <span class="ow">in</span> <span class="n">enabled_precisions</span>
        <span class="p">):</span>
            <span class="n">lower_precision</span> <span class="o">=</span> <span class="n">LowerPrecision</span><span class="o">.</span><span class="n">FP16</span>
        <span class="k">elif</span> <span class="p">(</span>
            <span class="n">torch</span><span class="o">.</span><span class="n">float32</span> <span class="ow">in</span> <span class="n">enabled_precisions</span>
            <span class="ow">or</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">float</span> <span class="ow">in</span> <span class="n">enabled_precisions</span>
        <span class="p">):</span>
            <span class="n">lower_precision</span> <span class="o">=</span> <span class="n">LowerPrecision</span><span class="o">.</span><span class="n">FP32</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Precision </span><span class="si">{</span><span class="n">enabled_precisions</span><span class="si">}</span><span class="s2"> not supported on FX&quot;</span><span class="p">)</span>

        <span class="k">return</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span>
            <span class="n">module</span><span class="p">,</span>
            <span class="n">inputs</span><span class="p">,</span>
            <span class="n">lower_precision</span><span class="o">=</span><span class="n">lower_precision</span><span class="p">,</span>
            <span class="n">max_batch_size</span><span class="o">=</span><span class="n">inputs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span>
            <span class="n">explicit_batch_dimension</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
            <span class="n">dynamic_batch</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
            <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
        <span class="p">)</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;Module is an unknown format or the ir requested is unknown&quot;</span><span class="p">)</span></div>


<div class="viewcode-block" id="convert_method_to_trt_engine"><a class="viewcode-back" href="../../py_api/torch_tensorrt.html#torch_tensorrt.convert_method_to_trt_engine">[docs]</a><span class="k">def</span> <span class="nf">convert_method_to_trt_engine</span><span class="p">(</span>
    <span class="n">module</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span>
    <span class="n">method_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
    <span class="n">ir</span><span class="o">=</span><span class="s2">&quot;default&quot;</span><span class="p">,</span>
    <span class="n">inputs</span><span class="o">=</span><span class="p">[],</span>
    <span class="n">enabled_precisions</span><span class="o">=</span><span class="nb">set</span><span class="p">([</span><span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">float</span><span class="p">]),</span>
    <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
<span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;Convert a TorchScript module method to a serialized TensorRT engine</span>

<span class="sd">    Converts a specified method of a module to a serialized TensorRT engine given a dictionary of conversion settings</span>

<span class="sd">    Arguments:</span>
<span class="sd">        module (Union(torch.nn.Module,torch.jit.ScriptModule): Source module</span>

<span class="sd">    Keyword Arguments:</span>
<span class="sd">        inputs (List[Union(torch_tensorrt.Input, torch.Tensor)]): **Required** List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using</span>
<span class="sd">            torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum</span>
<span class="sd">            to select device type. ::</span>

<span class="sd">                input=[</span>
<span class="sd">                    torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1</span>
<span class="sd">                    torch_tensorrt.Input(</span>
<span class="sd">                        min_shape=(1, 224, 224, 3),</span>
<span class="sd">                        opt_shape=(1, 512, 512, 3),</span>
<span class="sd">                        max_shape=(1, 1024, 1024, 3),</span>
<span class="sd">                        dtype=torch.int32</span>
<span class="sd">                        format=torch.channel_last</span>
<span class="sd">                    ), # Dynamic input shape for input #2</span>
<span class="sd">                    torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings</span>
<span class="sd">                ]</span>

<span class="sd">        enabled_precision (Set(Union(torch.dtype, torch_tensorrt.dtype))): The set of datatypes that TensorRT can use when selecting kernels</span>
<span class="sd">        ir (str): The requested strategy to compile. (Options: default - Let Torch-TensorRT decide, ts - TorchScript with scripting path)</span>
<span class="sd">        **kwargs: Additional settings for the specific requested strategy (See submodules for more info)</span>
<span class="sd">    Returns:</span>
<span class="sd">        bytes: Serialized TensorRT engine, can either be saved to a file or deserialized via TensorRT APIs</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">module_type</span> <span class="o">=</span> <span class="n">_parse_module_type</span><span class="p">(</span><span class="n">module</span><span class="p">)</span>
    <span class="n">target_ir</span> <span class="o">=</span> <span class="n">_get_target_ir</span><span class="p">(</span><span class="n">module_type</span><span class="p">,</span> <span class="n">ir</span><span class="p">)</span>
    <span class="k">if</span> <span class="n">target_ir</span> <span class="o">==</span> <span class="n">_IRType</span><span class="o">.</span><span class="n">ts</span><span class="p">:</span>
        <span class="n">ts_mod</span> <span class="o">=</span> <span class="n">module</span>
        <span class="k">if</span> <span class="n">module_type</span> <span class="o">==</span> <span class="n">_ModuleType</span><span class="o">.</span><span class="n">nn</span><span class="p">:</span>
            <span class="n">logging</span><span class="o">.</span><span class="n">log</span><span class="p">(</span>
                <span class="n">logging</span><span class="o">.</span><span class="n">Level</span><span class="o">.</span><span class="n">Info</span><span class="p">,</span>
                <span class="s2">&quot;Module was provided as a torch.nn.Module, trying to script the module with torch.jit.script. In the event of a failure please preconvert your module to TorchScript&quot;</span><span class="p">,</span>
            <span class="p">)</span>
            <span class="n">ts_mod</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span><span class="p">(</span><span class="n">module</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">ts</span><span class="o">.</span><span class="n">convert_method_to_trt_engine</span><span class="p">(</span>
            <span class="n">ts_mod</span><span class="p">,</span>
            <span class="n">method_name</span><span class="p">,</span>
            <span class="n">inputs</span><span class="o">=</span><span class="n">inputs</span><span class="p">,</span>
            <span class="n">enabled_precisions</span><span class="o">=</span><span class="n">enabled_precisions</span><span class="p">,</span>
            <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
        <span class="p">)</span>
    <span class="k">elif</span> <span class="n">target_ir</span> <span class="o">==</span> <span class="n">_IRType</span><span class="o">.</span><span class="n">fx</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;fx is currently not supported&quot;</span><span class="p">)</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;Module is an unknown format or the ir requested is unknown&quot;</span><span class="p">)</span></div>
</pre></div>

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